<i>In Vitro</i> and <i>In Vivo</i> Studies of a New Class of Anticancer Molecules for Targeted Radiotherapy of Cancer
Bibliographic record
Abstract
There is a compelling need to develop anticancer therapies that target cancer cells and tissues. Arising from innovative femtomedicine studies, a new class of non-platinum-based halogenated molecules (called FMD molecules) that selectively kill cancer cells and protect normal cells in treatments of multiple cancers has been discovered. This article reports the first observation of the radiosensitizing effects of such compounds in combination with ionizing radiation for targeted radiotherapy of a variety of cancers. We present in vitro and in vivo studies focused on combination with radiotherapy of cervical, ovarian, head and neck, and lung cancers. Our results demonstrate that treatments of various cancer cells in vitro and in vivo mouse xenograft models with such compounds led to enhanced efficiencies in radiotherapy, while the compounds themselves induced no or little radiotoxicity toward normal cells or tissues. These compounds are therefore effective radiosensitizers that can be translated into clinical trials for targeted radiotherapy of multiple types of cancer. This study also shows the potential of femtomedicine to bring breakthroughs in understanding fundamental biologic processes and to accelerate the discovery of novel drugs for effective treatment or prevention of a variety of cancers. Mol Cancer Ther; 15(4); 640-50. ©2016 AACR.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".